Here is the detection script: https://github.com/opencv/opencv/blob/master/cmake/OpenCVDetectInferenceEngine.cmake Usually it is enough to set WITH_INF_ENGINE to ON. If it can not be detected try to set InferenceEngine_DIR option (e.g. "<openvino>/deployment_tools/inference_engine/share"...
OpenCV modules: To be built: alphamat aruco bgsegm bioinspired calib3d ccalib core datasets dnn dnn_objdetect dnn_superres dpm face features2d flann freetype fuzzy gapi hdf hfs highgui img_hash imgcodecs imgproc intensity_transform line_descriptor mcc ml objdetect optflow phase_unwrapping photo pl...
VERSION=1.8.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.9.1+cu111 OpenCV: 4.10.0 MMEngine: 0.10.4 MMRotate: 1.0.0rc1+3ff004e Reproduce...
I am using OpenCV(4.1.2-openvino) , when I am trying to write video output I am getting below error. Although my output video is still getting generated. Below is cv2.VideoWriter statement and full error signature. Please let me know how to resolve this. out_video=cv2.Vide...
OpenCV: 4.5.0 MMCV: 1.5.3 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler: 10.2 MMDetection: 2.25.0+56e42e7 Error traceback Click to expand /usr/local/lib/python3.6/dist-packages/torchvision/io/image.py:11: UserWarning: Failed to load image Python extension: ...
tensorflow-keras: LSTM层警告 Layer lstm will not use cuDNN kernel since it doesn‘t meet the cuDNN kern 训练LSTM网络的时候遇到这个警告,大致就是无法在cuDNN的加速下使用,看的出训练的速度很慢。 下面是我的网络结构,很简单,就用了一层LSTM,定义了**函数是relu。 找了一下官网文档对 LSTM 的API如何...
基于OpenCV深度学习神经网络人脸模块(OpenCV DNN Face)的实时人脸识别程序.zip 人工智能-项目实践-深度学习 上传者:admin_maxin时间:2024-02-19 CentOS7 下 GPU 安装配置指南 及 TensorFlow : Openface 的 GPU 使用.pdf CentOS7 下 GPU 安装配置指南 及 TensorFlow : Openface 的 GPU 使用.pdf ...
USE_CUDNN=ON, USE_EIGEN_FOR_BLAS=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=OFF, USE_MKLDNN=OFF, USE_MPI=ON, USE_NCCL=0, USE_NNPACK=ON, USE_OPENMP=ON, TorchVision: 0.10.0a0+300a8a4 OpenCV: 4.5.0 MMCV: 1.5.3 MMCV Compiler: GCC 7.5 MMCV CUDA Compiler:...
-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_...
I've tried different networks, different accuracies within networks, the OpenCV DNN extension, different image sizes and different datatypes. Nothing increased the accuracy to what the CPU has. I am wondering if I'm doing something wrong, or if I'm not...